Multiplayer Game

Multiplayer game research focuses on understanding and improving various aspects of these complex systems, primarily aiming to enhance game balance, player experience, and fairness. Current research employs diverse techniques, including machine learning models like deep reinforcement learning, Bayesian methods, and game-theoretic approaches (e.g., Nash equilibrium analysis), to analyze player behavior, predict skill levels, detect cheating, and optimize matchmaking. These advancements have implications for game design, improving game balance and player experience, and also contribute to broader fields like multi-agent systems and optimization algorithms.